# A dataset of over one thousand computed tomography scans of battery cells

arXiv: https://arxiv.org/abs/2403.02527 · HTML: https://mlsanity.com/paper/2403.02527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation](https://mlsanity.com/paper/2410.07458/) (2024)

## Abstract (arXiv)

Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.